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Lavanya Raman

Publications and source records attributed to Lavanya Raman.

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Decoupling dislocation multiplication and velocity effects in metals at extreme strain rates

The dynamic behavior of metals is governed by collective dislocation motion and interactions that strongly depend on the applied strain rate. Metals exhibit weak strain rate sensitivity (SRS) below a certain threshold, followed by a distinct SRS upturn at higher loading rates. While this upturn is typically attributed to increased glide resistance at high dislocation velocity due to mechanisms such as phonon drag, the role of strain-rate-dependent dislocation multiplication and microstructural evolution under these extreme conditions remains elusive. Here, we decouple these two strengthening effects and show that, while dislocation velocity primarily governs the SRS upturn, the hardening due to microstructure evolution depends strongly on the initial dislocation density. Our investigation of hardness evolution across ten decades of strain rates in a quenched and tempered martensitic low-carbon steel (LCS) using laser-induced projectile impact tests (LIPIT) and nanoindentation reveals SRS upturn around 10^7 1/s. By performing in situ re-indentation of the formed craters, we probe the contribution of dislocations generated during initial deformation at different strain rates. We show that while dislocation multiplication plays a negligible role in fine-grained LCS with high dislocation density, a pronounced dislocation multiplication contributes to the hardness increase in pure iron with lower initial dislocation density. Our results show that, depending on the initial microstructure of metals, dislocation multiplication significantly governs high-strain-rate plasticity, in addition to dislocation velocity effects.

cond-mat.mtrl-sci

Comparing Forward and Inverse Design Paradigms: A Case Study on Refractory High-Entropy Alloys

The rapid design of advanced materials is a topic of great scientific interest. The conventional, ``forward'' paradigm of materials design involves evaluating multiple candidates to determine the best candidate that matches the target properties. However, recent advances in the field of deep learning have given rise to the possibility of an ``inverse'' design paradigm for advanced materials, wherein a model provided with the target properties is able to find the best candidate. Being a relatively new concept, there remains a need to systematically evaluate how these two paradigms perform in practical applications. Therefore, the objective of this study is to directly, quantitatively compare the forward and inverse design modeling paradigms. We do so by considering two case studies of refractory high-entropy alloy design with different objectives and constraints and comparing the inverse design method to other forward schemes like localized forward search, high throughput screening, and multi objective optimization.

cond-mat.mtrl-sci